Methodology of Sentimental Analysis

Our algorithms restlessly get feed directly from Twitter and continuously scan business news feeds and blogs, searching for equities, funds and company mentions. We determine which content belongs to particular company and assign ‘relevance’ score. If ‘relevance’ score is low, the tie is discarded. Only a strong connected content are kept for further analysis.

We apply NLP (natural language processing) with Bayesian filters to high relevant articles in order to determine sentiments behind the words. After all we keep aggregated sentiment scores and volumes for further analyses, data visualizations and other actions for your success.

INDICATOR DESCRIPTIONS

Tweets Ratio:a measure of the positive / negative (or bullishness / bearishness) mentions used for a given equity for a given period. Possible values are [0:1) for negative mentions, (1:∞) for positive mentions. The highest tweets ratio indicates the most positive signal for certain equity for the period. We found significant statistical correlation between stock prices/trends and sentimental ratios.

Positive Signal: the tweet ratio above 1.0.

Negative Signal: the tweet ratio below 1.0

Neutral Signal: the signal with tweet ratio equal 1.0

Volume Ratio: Normalized sum of articles, blog posts and tweets about the particular equity, published and analyzed on a given day. Volume Ratio is a good indicator of a trend in both directions: upward and downward. We measure not an absolute value but the change in the standard deviation of news, blogs and tweets volume. The best perception for Volume Ratio as an indicator is the ‘rate of change of social buzz’.